Real time self-adjusting calibration algorithm

ABSTRACT

A method of calibrating glucose monitor data includes collecting the glucose monitor data over a period of time at predetermined intervals. It also includes obtaining at least two reference glucose values from a reference source that temporally correspond with the glucose monitor data obtained at the predetermined intervals. Also included is calculating the calibration characteristics using the reference glucose values and the corresponding glucose monitor data to regress the obtained glucose monitor data. And calibrating the obtained glucose monitor data using the calibration characteristics is included. In preferred embodiments, the reference source is a blood glucose meter, and the at least two reference glucose values are obtained from blood tests. In additional embodiments, the calculation of the calibration characteristics is obtained using linear regression and in particular embodiments, least squares linear regression. Alternatively, the calculation of the calibration characteristics is obtained using non-linear regression.

RELATED APPLICATIONS

[0001] This application is a continuation-in-part of U.S. patentapplication Ser. No. 09/511,580, filed Feb. 23, 2000 and entitled“Glucose Monitor Calibration Methods”, which is herein incorporated byreference in its entirety.

FIELD OF THE INVENTION

[0002] This invention relates to glucose monitor systems and, inparticular embodiments, to calibration methods for glucose monitoringsystems.

BACKGROUND OF THE INVENTION

[0003] Over the years, body characteristics have been determined byobtaining a sample of bodily fluid. For example, diabetics often testfor blood glucose levels. Traditional blood glucose determinations haveutilized a painful finger prick using a lancet to withdraw a small bloodsample. This results in discomfort from the lancet as it contacts nervesin the subcutaneous tissue. The pain of lancing and the cumulativediscomfort from multiple needle pricks is a strong reason why patientsfail to comply with a medical testing regimen used to determine a changein a body characteristic over a period of time. Although non-invasivesystems have been proposed, or are in development, none to date havebeen commercialized that are effective and provide accurate results. Inaddition, all of these systems are designed to provide data at discretepoints and do not provide continuous data to show the variations in thecharacteristic between testing times.

[0004] A variety of implantable electrochemical sensors have beendeveloped for detecting and/or quantifying specific agents orcompositions in a patient's blood. For instance, glucose sensors arebeing developed for use in obtaining an indication of blood glucoselevels in a diabetic patient. Such readings are useful in monitoringand/or adjusting a treatment regimen which typically includes theregular administration of insulin to the patient. Thus, blood glucosereadings improve medical therapies with semi-automated medicationinfusion pumps of the external type, as generally described in U.S. Pat.Nos. 4,562,751; 4,678,408; and 4,685,903; or automated implantablemedication infusion pumps, as generally described in U.S. Pat. No.4,573,994, which are herein incorporated by reference. Typical thin filmsensors are described in commonly assigned U.S. Pat. Nos. 5,390,671;5,391,250; 5,482,473; and 5,586,553 which are incorporated by referenceherein. See also U.S. Pat. No. 5,299,571.

SUMMARY OF THE DISCLOSURE

[0005] It is an object of an embodiment of the present invention toprovide an improved glucose monitor system and method, which obviatesfor practical purposes, the above mentioned limitations.

[0006] According to an embodiment of the invention, a method ofcalibrating glucose monitor data includes obtaining glucose monitor dataat predetermined intervals over a period of time. It also includesobtaining at least two reference glucose values from a reference sourcethat correspond with the glucose monitor data obtained at thepredetermined intervals. Additionally, calculating calibrationcharacteristics using the at least two reference values and thecorresponding glucose monitor data to regress the obtained glucosemonitor data is included. And calibrating the obtained glucose monitordata using the calibration characteristics is included. In preferredembodiments, the reference source is a blood glucose meter, and the atleast two reference glucose values are obtained from blood tests. Inadditional embodiments, the calculation of the calibrationcharacteristics is obtained using linear regression, and in particularembodiments, using least squares linear regression. Alternatively, thecalculation of the calibration characteristics is obtained usingnon-linear regression or a non-regression technique.

[0007] In particular embodiments, the predetermined period of time is a24 hour period, and the predetermined intervals are 5 minute intervals.Further embodiments may include the step of shifting the data by apredetermined time factor, such as for example, ten minutes. Preferably,the calibration is performed while obtaining glucose monitor data.However, alternative embodiments may perform the calibration on glucosemonitor data that has been collected for post processing by anotherprocessing device.

[0008] According to an embodiment of the invention, a method ofcalibrating glucose monitor data includes obtaining glucose monitor dataat a predetermined memory storage rate. Also included is obtaining atleast one blood glucose reference reading from a blood glucose measuringdevice that corresponds with at least one glucose monitor data pointobtained at the predetermined memory storage rate. Calculating acalibration factor using the at least one blood glucose referencereading and the corresponding at least one glucose monitor data point isincluded. And calibrating the obtained glucose monitor data using thecalibration factor is included. In preferred embodiments, after a firstcalibration factor is calculated, at least one previous calibrationfactor is used with at least one blood glucose reference reading from ablood glucose measuring device and its at least one correspondingglucose monitor data point to calculate a calibration factor. Inadditional embodiments, at least two blood glucose reference readingsare used for calibration. In further embodiments, the calculation of thecalibration factor is obtained using linear regression, and inparticular least squares linear regression. Alternatively, calculationof the calibration factor uses non-linear regression or a non-regressiontechnique

[0009] In particular embodiments, the calibration factor is applied toglucose monitor data obtained before a last blood glucose referencereading from a blood glucose measuring device that corresponds with atleast one glucose monitor data point obtained at a predetermined memorystorage rate is used to calculate the calibration factor. Alternatively,the calibration factor is applied to glucose monitor data obtained afterthe last blood glucose reference reading from a blood glucose measuringdevice that is used to calculate the calibration factor.

[0010] In particular embodiments, the predetermined memory storage rateis once every 5 minutes. And the glucose monitor data that is obtainedat a predetermined memory storage rate is the result of utilizing atleast 2 sample values sampled from a glucose sensor at a rate fasterthan the memory storage rate.

[0011] In preferred embodiments, at least one blood glucose referencereading from a blood glucose measuring device is obtained during apredetermined calibration period, and a calibration factor is calculatedusing those readings after every predetermined calibration period. Inparticular embodiments, the predetermined calibration period is 24hours. In further preferred embodiments, a predetermined time shift isused to temporally correlate the at least one blood glucose referencereading from a blood glucose measuring device with the at least oneglucose monitor data point obtained at the predetermined memory storagerate. In particular embodiments, the predetermined time shift is tenminutes.

[0012] In particular embodiments, one or more calculations forcalculating a first calibration factor is different from the one or morecalculations for calculating subsequent calibration factors. In otherparticular embodiments, the calculation for calculating a firstcalibration factor uses a single-point calibration equation. In furtherparticular embodiments, the single-point calibration equation includesan offset value. In other particular embodiments, the one or morecalculations for calculating a calibration factor other than the firstcalibration factor uses a linear regression calibration equation, anon-linear regression calibration equation, or a non-regressiontechnique.

[0013] According to an embodiment of the invention, a method ofcalibrating glucose monitor data includes obtaining glucose monitordata. It also includes obtaining from another blood glucose measuringdevice at least one blood glucose reference reading that is temporallyassociated with at least one glucose monitor data reading. Determining acalibration equation using the at least one blood glucose referencereading and the corresponding at least one glucose monitor data readingis also included. And calibrating the glucose monitor data using thecalibration equation is included.

[0014] According to another embodiment of the invention, a method ofcalibrating body characteristic monitor data includes obtaining bodycharacteristic monitor data. It also includes obtaining from anothercharacteristic measuring device at least one characteristic referencereading that is temporally associated with at least one characteristicmonitor data point. Calculating calibration characteristics using the atleast one characteristic reference reading and the corresponding atleast one characteristic monitor data point is included. And calibratingthe obtained characteristic monitor data using the calibrationcharacteristics is included. In particular embodiments, at least twobody characteristic reference readings are used for calculating thecalibration characteristics. In particular embodiments, the calculationfor calculating the calibration characteristics is a linear regressioncalculation.

[0015] According to additional embodiments of the invention, anapparatus for calibrating glucose monitor data includes a glucosemonitor, glucose sensor, a blood glucose meter and a processor. Theglucose monitor includes a glucose monitor memory for storing glucosemonitor data. The glucose sensor is electronically coupled to theglucose monitor to supply the glucose monitor data. The blood glucosemeasuring device provides at least one blood glucose reference readingthat is temporally associated with at least one glucose monitor datapoint. And the processor includes software to calculate calibrationcharacteristics using the at least one blood glucose reference readingthat is temporally associated with at least one glucose monitor datapoint, and the processor applies the calibration characteristics to theglucose monitor data. In particular embodiments, the at least one bloodglucose reading is entered into the glucose monitor. In particularembodiments, the glucose monitor includes the processor, oralternatively, the processor is in a separate device that receivesglucose monitor data from the glucose monitor.

[0016] In other embodiments of the invention, an apparatus forcalibrating glucose monitor data includes means for obtaining glucosemonitor data. It also includes means for obtaining from another bloodglucose measuring device at least one blood glucose reference readingthat is temporally associated with at least one glucose monitor datareading. Means for calculating a calibration equation using the at leastone blood glucose reference reading and the corresponding at least oneglucose monitor data reading is included. And means for calibrating theglucose monitor data using the calibration equation is also included.

[0017] Other features and advantages of the invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings which illustrate, by way of example,various features of embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] A detailed description of embodiments of the invention will bemade with reference to the accompanying drawings, wherein like numeralsdesignate corresponding parts in the several figures.

[0019]FIG. 1 is a is a perspective view illustrating a subcutaneousglucose sensor insertion set and glucose monitor device in accordancewith an embodiment of the present invention;

[0020]FIG. 2 is a cross-sectional view of the sensor set and glucosemonitor device as shown along the line 2-2 of FIG. 1;

[0021]FIG. 3 is a cross-sectional view of a slotted insertion needleused in the insertion set of FIGS. 1 and 2;

[0022]FIG. 4 is a cross-sectional view as shown along line 4-4 of FIG.3;

[0023]FIG. 5 is a cross-sectional view as shown along line 5-5 of FIG.3;

[0024]FIG. 6 is a partial cross-sectional view corresponding generallywith the encircled region 6 of FIG. 2;

[0025]FIG. 7 is a cross- sectional view as shown along line 7-7 of FIG.2;

[0026] FIGS. 8(a-c) are diagrams showing a relationship between sampledvalues, interval values and memory storage values;

[0027]FIG. 9 is a chart showing clipping limits;

[0028]FIG. 10 is a sample computer screen image of a post processoranalysis of glucose monitor data;

[0029]FIG. 11 is a chart illustrating the pairing of a blood glucosereference reading with glucose monitor data;

[0030]FIG. 12 is a chart illustrating an example of a single-pointcalibration;

[0031]FIG. 13 is a block diagram of a single-point calibrationtechnique;

[0032]FIG. 14 is a chart illustrating an example of a linear regressioncalibration.

[0033]FIG. 15 is a block diagram of a linear regression calibrationtechnique;

[0034]FIG. 16 is a flowchart of a self-adjusting calibration techniquein accordance with an embodiment of the present invention;

[0035]FIGS. 17a and 17 b are charts illustrating an example of theself-adjusting calibration technique in accordance with FIG. 16; and

[0036]FIGS. 18a and 18 b are further charts illustrating an example ofthe self-adjusting calibration technique in accordance with FIG. 16.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0037] As shown in the drawings for purposes of illustration, theinvention is embodied in calibration methods for a glucose monitor thatis coupled to a sensor set to provide continuous data recording ofreadings of glucose levels from a sensor for a period of time. Inpreferred embodiments of the present invention, the sensor and monitorare a glucose sensor and a glucose monitor for determining glucoselevels in the blood and/or bodily fluids of a user. However, it will berecognized that further embodiments of the invention may be used todetermine the levels of other body characteristics including analytes oragents, compounds or compositions, such as hormones, cholesterol,medications concentrations, viral loads (e.g., HIV), bacterial levels,or the like. The glucose sensor is primarily adapted for use insubcutaneous human tissue. However, in still further embodiments, one ormore sensors may be placed in other tissue types, such as muscle, lymph,organ tissue, veins, arteries or the like, and used in animal tissue tomeasure body characteristics. Embodiments may record readings from thesensor on an intermittent, periodic, on-demand, continuous, or analogbasis.

[0038] FIGS. 1-7 illustrate a glucose monitor system 1 for use with thecalibration methods. The glucose monitor system 1, in accordance with apreferred embodiments of the present invention, includes a subcutaneousglucose sensor set 10 and a glucose monitor 100. In preferredembodiments, the glucose monitor 100 is of the type described in U.S.patent application Ser. No. 60/121,664, filed on Feb. 25, 1999, entitled“Glucose Monitor System”, which is herein incorporated by reference. Inalternative embodiments, the glucose monitor is of the type described inU.S. patent application Ser. No. 09/377,472, filed Aug. 19, 1999,entitled “Telemetered Characteristic Monitor System And Method Of UsingThe Same”, which is incorporated by reference herein.

[0039] Preferably, the glucose monitor 100 is worn by the user and isconnected to a surface mounted glucose sensor set 10 that is attached toa user's body by an electrically conductive cable 102, of the typedescribed in U.S. patent application Ser. No. 60/121,656, filed on Feb.25, 1999, entitled “Test Plug and Cable for a Glucose Monitor”, which isincorporated by reference herein. In preferred embodiments, the sensorinterface may be configured in the form of a jack to accept differenttypes of cables that provide adaptability of the glucose monitor 100 towork with different types of subcutaneous glucose sensors and/or glucosesensors placed in different locations of the user's body. However, inalternative embodiments, the sensor interface is permanently connectedto the cable 102. In additional alternative embodiments, acharacteristic monitor is connected to one or more sensor sets to recorddata of one or more body characteristics from one or more locations onor in the user's body.

[0040] The glucose sensor set 10 is of the type described in U.S. patentapplication Ser. No. 60/121,655, filed on Feb. 25, 1999, entitled“Glucose Sensor Set”, or U.S. patent application Ser. No. 08/871,831,filed on Jun. 9, 1997, entitled “Insertion Set For A TranscutaneousSensor”, which are incorporated by reference herein. The glucose sensor12, of the type described in U.S. patent application Ser. No.29/101,218, filed on Feb. 25, 1999, entitled “Glucose Sensor”, ordescribed in commonly assigned U.S. Pat. Nos. 5,390,671; 5,391,250;5,482,473; and 5,586,553 which are incorporated by reference herein;extends from the glucose sensor set 10 into the user's body withelectrodes 20 of the glucose sensor 12 terminating in the user'ssubcutaneous tissue. See also U.S. Pat. No. 5,299,571. However, inalternative embodiments, the glucose sensor 12 may use other types ofsensors, such as chemical based, optical based, or the like. In furtheralternative embodiments, the sensors may be of a type that is used onthe external surface of the skin or placed below the skin layer of theuser for detecting body characteristics.

[0041] The glucose monitor 100 generally includes the capability torecord and store data as it is received from the glucose sensor 12, andincludes either a data port (not shown) or wireless transmitter and/orreceiver (also not shown) for transferring data to and/or from a dataprocessor 200 such as a computer, communication station, a dedicatedprocessor designed specifically to work with the glucose monitor, or thelike. The glucose monitor is generally of the type described in U.S.patent application Ser. No. 09/377,472, filed Aug. 19, 1999, entitled“Telemetered Characteristic Monitor System And Method of Using TheSame”, which is incorporated by reference herein.

[0042] Preferably, the glucose monitor system 1 minimizes inconvenienceby separating complicated monitoring process electronics into twoseparate devices; the glucose monitor 100, which attaches to the glucosesensor set 10; and the data processor 200, which contains the softwareand programming instructions to download and evaluate data recorded bythe glucose monitor 100. In addition, the use of multiple components(e.g., glucose monitor 100 and data processor 200) facilitates upgradesor replacements, since one module, or the other, can be modified,re-programmed, or replaced without requiring complete replacement of themonitor system 1. Further, the use of multiple components can improvethe economics of manufacturing, since some components may requirereplacement on a more frequent basis, sizing requirements may bedifferent for each module, different assembly environment requirements,and modifications can be made without affecting the other components.

[0043] The glucose monitor 100 takes raw glucose sensor data from theglucose sensor 12 and assesses it during real-time and/or stores it forlater processing or downloading to the data processor 200, which in turnanalyzes, displays, and logs the received data. The data processor 200utilizes the recorded data from the glucose monitor. 100 to analyze andreview the blood glucose history. In particular embodiments, the glucosemonitor 100 is placed into a com-station which facilitates downloadingdata to a personal computer for presentation to a physician. A softwareis used to download the data, create a data file, calibrate the data,and display the data in various formats including charts, forms,reports, graphs, tables, lists, and the like. In further embodiments,the glucose monitor system 1 may be used in a hospital environment orthe like.

[0044] In alternative embodiments, the glucose monitor includes at leastportions of the software described as contained within the dataprocessor 200 above. The glucose monitor might contain the necessarysoftware to calibrate glucose sensor signals, display a real-time bloodglucose value, show blood glucose trends, activate alarms and the like.A glucose monitor with these added capabilities is useful for patientsthat might benefit from real-time observations of their blood glucosecharacteristics even while they're not in close proximity to a computer,communication device or dedicated independent data processor.

[0045] As shown in FIG. 2, the data processor 200, may include a display214 that is used to display the calculated results of the raw glucosesensor data received via a download from the glucose monitor 100. Theresults and information displayed includes, but is not limited to,trending information of the characteristic (e.g., rate of change ofglucose), graphs of historical data, average characteristic levels(e.g., glucose), stabilization and calibration information, raw data,tables (showing raw data correlated with the date, time, sample number,corresponding blood glucose level, alarm messages, and more), and thelike. Alternative embodiments include the ability to scroll through thedata. The display 214 may also be used with buttons (not shown) on thedata processor 200, computer, communication station, characteristicmonitor, or the like, to program or update data. In preferredembodiments, the glucose monitor 100 includes a display 132 to assistthe user in programming the glucose monitor 100, entering data,stabilizing, calibrating, downloading data, or the like.

[0046] Still further embodiments of the present invention may includeone or more buttons 122, 124, 126 and 128 on the glucose monitor 100 toprogram the monitor 100, to record data, insert flags to correlate datawith external events for later analysis, input calibration values, orthe like. In addition, the glucose monitor 100 may include an on/offbutton 130 for compliance with safety standards and regulations totemporarily suspend transmissions or recording. The glucose monitor 100may also be combined with other medical devices to accept other patientdata through a common data network and/or telemetry system. The glucosemonitor 100 may be combined with a blood glucose meter to directlyimport or correlate glucose calibration reference values such asdescribed in U.S. patent application Ser. No. 09/334,996, filed Jun. 17,1999, entitled “Characteristic Monitor With A Characteristic Meter andMethod Of Using The Same”, which is incorporated by reference herein.The glucose monitor 100 may also be combined with semi-automatedmedication infusion pumps of the external type, as generally describedin U.S. Pat. Nos. 4,562,751; 4,678,408; and 4,685,903; or automatedimplantable medication infusion pumps, as generally described in U.S.Pat. No. 4,573,994, which are herein incorporated by reference. Theglucose monitor 100 may record data from the infusion pumps and/or mayprocess data from both the glucose sensor 12 and an infusion pump toestablish a closed loop system to control the infusion pump based onglucose sensor measurements. In other embodiments, other bodycharacteristics are monitored, and the monitor may be used to providefeedback in a closed loop system to control a drug delivery rate. Infurther alternative embodiments, the glucose monitor 100 can be combinedwith a glucose sensor set 10 as a single unit.

[0047] Glucose sensors are replaced periodically to avoid infection,decaying enzyme coating and therefore sensor sensitivity, deoxidizationof the electrodes, and the like. The user will disconnect the glucosesensor set 10 from the cable 102 and glucose monitor 100. A needle 14 isused to install another glucose sensor set 10 and then the needle 14 isremoved. Further description of the needle 14 and the sensor set 10 arefound in U.S. Pat. No. 5,586,553, entitled “Transcutaneous SensorInsertion Set”; co-pending U.S. patent application Ser. No. 09/346,835,filed Jul. 2, 1999, entitled “Insertion Set For A TranscutaneousSensor”; and U.S. Pat. No. 5,951,521, entitled “A SubcutaneousImplantable Sensor Set Having The Capability To Remove Or Deliver FluidsTo An Insertion Site,” which are herein incorporated by reference.

[0048] The user connects the connection portion 24 of the glucose sensorset 10 through the cable 102 to the glucose monitor 100, so that theglucose sensor 12 can then be used over a prolonged period of time. Aninitial reading may be downloaded from the glucose sensor set 10 and theglucose monitor 100 to the data processor 200, to verify properoperation of the glucose sensor 10 and the glucose monitor 100. Inpreferred embodiments, the glucose sensor set 10 provides data to theglucose monitor 100 for one to seven days before replacement. Glucosesensors 12 may last in the user's body for longer or shorter periods oftime depending on the quality of the installation, cleanliness, thedurability of the enzyme coating, deoxidization of the sensor, user'scomfort, and the like.

[0049] After installation into the body, the glucose sensor 12 isinitialized to achieve a steady state of operation before starting acalibration process. Preferably, power supplied by three series silveroxide 357 battery cells 110 in the glucose monitor 100 is used to speedthe initialization of the glucose sensor 12. Alternatively, other powersupplies may be used such as, different battery chemistries includinglithium, alkaline, or the like, and different numbers of batteries,solar cells, a DC converter plugged into an AC socket (provided withproper electrical isolation), or the like.

[0050] The use of an initialization process can reduce the time forglucose sensor 12 stabilization from several hours to an hour or less.The preferred initialization procedure uses a two step process. First, ahigh voltage (preferably between 1.0-1.1 volts—although other voltagesmay be used) is applied between electrodes 20 of the sensor 12 for oneto two minutes (although different time periods may be used) to allowthe sensor 12 to stabilize. Then, a lower voltage (preferably between0.5-0.6 volts—although other voltages may be used) is applied for theremainder of the initialization process (typically 58 minutes or less).Other stabilization/initialization procedures using differing currents,currents and voltages, different numbers of steps, or the like, may beused. Other embodiments may omit the initialization/stabilizationprocess, if not required by the body characteristic sensor or if timingis not a factor. Alternatively, the characteristic monitor or the dataprocessor 200 may apply an algorithm to the sensor data to determinewhen initial transients are sufficiently diminished and the sensor is ata significantly stable state to begin calibration.

[0051] In preferred embodiments, data is not considered valid until asensor initialization event flag (ESI) is set in the data indicatingthat stabilization is complete. Preferably, stabilization is completeafter 60 minutes or when a user enters a sensor initialization flagusing one or more buttons on the glucose monitor 100. Afterstabilization/initialization is complete the glucose monitor 100 iscalibrated to accurately interpret readings from the newly installedglucose sensor 12.

[0052] Beginning with the stabilization process, the glucose monitor 100measures a continuous electrical current signal (ISIG) generated by theglucose sensor 12 relative to a concentration of glucose present in thesubcutaneous tissue of the user's body. In preferred embodiments, theglucose monitor 100 samples the ISIG from the glucose sensor 12 at asampling rate of once every 10 seconds, as shown in FIGS. 8a-c. Examplesof sampled values are labeled A-AD in FIG. 8a. At an interval rate ofonce per minute, the highest and lowest of the sampled values (shown inFIG. 8a as circled sampled values A, E, G, I, M, R, V, W, Y, and AB) areignored, and the remaining 4 sampled values from an interval areaveraged to create interval values (shown in FIG. 8b as values F′, L′,R′, X′, and AD′). At a glucose monitor memory storage rate of once every5 minutes, the highest and lowest of the interval values (shown in FIG.8b as values L′ and X′) are ignored and the remaining 3 interval valuesare averaged and stored in a glucose monitor memory as memory values(shown in FIG. 8c as point AD″). The memory values are retained inmemory and may be downloaded to the data processor 200. The memoryvalues are used to calibrate the glucose monitor 100 and/or the postprocessor 200 and to analyze blood glucose levels. The sampling rate,interval rate and the memory storage rate may be varied as necessary tocapture data with sufficient resolution to observe transients or otherchanges in the data depending on the rate at which sensor values canchange, which is affected by the sensor sensitivity, the bodycharacteristic being measured, the physical status of the user, and thelike. In other embodiments, all of the sampled values are included inthe average calculations of memory storage values. In alternativeembodiments, more or less sampled values or interval values are ignoreddepending on the signal noise, sensor stability, or other causes ofundesired transient readings. Finally, in still other embodiments, allsampled values and/or interval values are stored in memory.

[0053] Clipping limits may be used to limit the signal magnitudevariation from one value to the next thereby reducing the effects ofextraneous data, outlying data points, or transients. In preferredembodiments, clipping limits are applied to the interval values. Forinstance, interval values that are above a maximum clipping limit orbelow a minimum clipping limit are replaced with the nearest clippinglimit value.

[0054] In alternative embodiments, interval values that are outside ofthe clipping limits are ignored and not used to calculate the nextmemory storage value. In particular embodiments, the detection ofinterval values outside of the clipping limits is considered acalibration cancellation event. In further particular embodiments, morethan one value must be deemed outside of clipping limits to constitute acalibration cancellation event. (Calibration cancellation events arediscussed below).

[0055] In preferred embodiments, the clipping limits are shifted aftereach data point. The level that the clipping limits are set to isdependent on an acceptable amount of change from the previous intervalvalue to the present interval value, which is affected by the sensorsensitivity, signal noise, signal drift, and the like. In preferredembodiments, the clipping limits are calculated based on the magnitudeof the previous interval value. For example, for a previous intervalvalue from 0 up to but not including 15 Nano-Amps, the clipping limitsare set at plus and minus 0.5 Nano-Amps about the previous intervalvalue. For a previous interval value from 15 up to but not including 25Nano-Amps, the clipping limits are set at plus and minus 3% of theprevious interval value, about the previous interval value. For aprevious interval value from 25 up to but not including 50 Nano-Amps,the clipping limits are set at plus and minus 2% of the previousinterval value, about the previous interval value. And for a previousinterval value of 50 Nano-Amps and greater, the clipping limits are setat plus and minus 1% about the previous interval value. In alternativeembodiments, different clipping limits may be used.

[0056]FIG. 9 shows a typical clipping limit example in which a previousinterval value 500, associated with interval N−1, has a magnitude of13.0 Nano-Amps, which is less than 15.0 Nano-Amps. Therefore, themaximum clipping limit 502 for the present interval value 506 is set at13.5 Nano-Amps, which is 0.5 Nano-Amps greater than the magnitude of theprevious interval value 500. And the minimum clipping limit 504 is setat 12.5 Nano-Amps which is 0.5 Nano-Amps below the previous intervalvalue 500. The present interval value 506, associated with interval N,is between the maximum clipping limit 502 and the minimum clipping limit504 and is therefore acceptable.

[0057] In another example shown in FIG. 9, the present interval value508, associated with interval M, has a value of 25.0 Nano-Amps which isoutside of the clipping limit 514 and will therefore be clipped. Theprevious interval value 510, associated with interval M−1, is 26.0Nano-Amps, which is included in the range from 25.0 up to but notincluding 50.0 Nano-Amps as discussed above. Therefore the clippinglimits are ±·2%. The maximum clipping limit 512 is 2% greater than theprevious interval value 510,

26.0+26.0*0.02=26.5 Nano-Amps.

[0058] Similarly the minimum clipping limit 514 is 2% less than theprevious interval value 510,

26.0−26.0*0.02=25.5 Nano-Amps.

[0059] Since the present interval value 508 of 25.0 Nano-Amps is lessthan the minimum clipping limit 514 of 25.5 Nano-Amps, it will beclipped, and 25.5 Nano-Amps will be used in place of 25.0 Nano-Amps tocalculate a memory storage value. For further illustration, FIG. 8 showsinterval value R′, which is calculated by averaging sampled values Nthrough Q, is outside of the clipping limits 412 and 414, which resultfrom the previous interval value L′. Therefore, the magnitude ofinterval value R′ is not used to calculate memory value AD″, instead R″,which is the magnitude of the minimum clipping limit 414, is used.

[0060] In other embodiments, the clipping limits may be a smaller orlarger number of Nano-Amps or a smaller or larger percentage of theprevious interval value based on the sensor characteristics mentionedabove. Alternatively, the clipping limits are calculated as plus orminus the same percent change from every previous interval value. Otheralgorithms use several interval values to extrapolate the next intervalvalue and set the clipping limits to a percentage higher and lower thanthe next anticipated interval value. In further alternatives, clippingmay be applied to the sampled values, interval values, memory values,calculated glucose values, estimated values of a measuredcharacteristic, or any combination of the values.

[0061] In preferred embodiments, all interval values are compared to anout-of-range limit of 200 Nano-Amps. If three consecutive intervalvalues are equal to or exceed the out-of-range limit, the sensorsensitivity is deemed to be too high and an alarm is activated to notifythe user that re-calibration is required or the sensor may needreplacing. In alternative embodiments, the out-of-range limit is set athigher or lower values depending on the range of sensor sensitivities,the expected working life of the sensor, the range of acceptablemeasurements, and the like. In particular embodiments, the out-of rangelimit is applied to the sampled values. In other embodiments, theout-of-range limit is applied to the memory storage values.

[0062] In preferred embodiments, unstable signal alarm limits are set todetect when memory storage values change too much from one to another.The signal alarm limits are established similarly to the clipping limitsdescribed above for the interval values, but allow for a larger changein value since there is more time between memory storage values thanbetween interval values. Re-calibration or replacement of the glucosesensor 12 is required once an unstable signal alarm is activated. Inessence, the glucose monitor 100 has detected too much noise in the ISIGfrom the glucose sensor 12.

[0063] Each memory storage value is considered valid (Valid ISIG value)unless one of the following calibration cancellation events occurs: anunstable signal alarm (as discussed above), a sensor initializationevent (as discussed above), a sensor disconnect alarm, a power on/offevent, an out-of-range alarm (as discussed above), or a calibrationerror alarm. Only Valid ISIG values are used to calculate blood glucoselevels by the glucose monitor 100 or post processor 200, as shown inFIG. 10. Once a calibration cancellation event occurs, the successivememory storage values are not valid, and therefore are not used tocalculate blood glucose, until the glucose monitor 100 or post processor200 is re-calibrated. FIG. 10 shows an explanatory computer screen inwhich cell P3 indicates a sensor disconnect alarm with the abbreviation“SeDi”. As shown, blood glucose values do not appear in column K, titled“Sensor Value”, and Valid ISIG values do not appear in column J untilafter the sensor is initialized, as indicated by the “ESI” flag in cellN17. One exception however, is the power on/off event. If the glucosemonitor 100 is turned off for a short enough period of time, generallyup to 30 minutes, the memory storage values are considered Valid ISIGvalues as soon as the power is turned back on. If the power is off forlonger than 30 minutes, the glucose monitor must be re-calibrated beforeISIG values are considered valid. Alternatively, the power may be off 30minutes up to indefinitely and once the power is restored, the memorystorage values are Valid ISIG values. The sensor disconnect alarm isactivated when the glucose monitor 100 does not detect a signal. Inpreferred embodiments, when 2 or more out of 5 interval values collectedwithin a given memory storage rate are less than 1.0 Nano-Amp, thedisconnect alarm is triggered. In alternative embodiments, more or lessvalues need be below a particular amperage to trigger the disconnectalarm depending of the acceptable range or sensor readings and thestability of the sensor signal. The remaining two calibrationcancellation events, the calibration error and an alternative embodimentfor the out-of-range alarm, are discussed in conjunction with thecalibration process below.

[0064] Preferred embodiments are directed to calibration techniques thatare used by either glucose monitors 100 during real-time measurements ofone or more signals from the glucose sensor 12, or post processors 200during post-processing of data that has been previously recorded anddownloaded (as shown in FIG. 10).

[0065] To calibrate the glucose monitor 100, the calibration factorcalled a sensitivity ratio (SR) (blood glucose level/Valid ISIG value)is calculated for a particular glucose sensor 12. The SR is acalibration factor used to convert the Valid ISIG value (Nano-Amps) intoa blood glucose level (mg/dl or mmol/l). In alternative embodiments, theunits for the SR may vary depending on the type of signal available fromthe sensor (frequency, amplitude, phase shift, delta, current, voltage,impedance, capacitance, flux, and the like), the magnitude of thesignals, the units to express the characteristic being monitored, or thelike.

[0066] In preferred embodiments, the user obtains a blood glucosereference reading from a common glucose meter, or another blood glucosemeasuring device, and immediately enters the blood glucose referencereading into the glucose monitor 100. The blood glucose referencereading is assumed to be accurate and is used as a reference forcalibration. The glucose monitor 100, or a post processor 200, musttemporally correlate the blood glucose reference reading with a ValidISIG value to establish a paired calibration data point. Since theglucose level in the interstitial body fluid tends to lag behind theblood glucose level, the glucose monitor 100 or post processor 200applies a delay time and then pairs the blood glucose reference readingwith a Valid ISIG value as shown in FIG. 11. In preferred embodiments,an empirically derived 10 minute delay is used. Since Valid ISIG valuesare averaged and stored every 5 minutes, the glucose monitor 100correlates the blood glucose reference reading with the third Valid ISIGstored in memory after the blood glucose reference reading is entered(resulting in an effective delay of 10 to 15 minutes). FIG. 11illustrates an example, in which a blood glucose reference reading 600of 90 mg/dl is entered into the glucose monitor 100 at 127 minutes. Thenext Valid ISIG value 602 is stored at 130 minutes. Given a 10 minutedelay, the glucose reference reading 600 is paired with the Valid ISIGvalue 604 which is stored at 140 minutes with a value of 30 Nano-amps.Note that two numbers are needed to establish one paired calibrationdata point, a blood glucose reference reading and a Valid ISIG.

[0067] Other delay times may be used depending on the user's metabolism,the response time of the sensor, the delay time required for the glucosemeter to calculate a reading and for the reading to be entered into theglucose monitor 100, the type of analyte being measured, the tissue thatthe sensor is placed into, environmental factors, whether the previousglucose Valid ISIG value (or the trend of the Valid ISIG values) washigher or lower than current Valid ISIG value, or the like. Once pairedcalibration data is available, the appropriate calibration process maybe applied dependent on how many paired calibration data points areavailable since the last calibration, the total period of time that theglucose sensor 12 has been in use, and the number of times the glucosesensor 12 has been calibrated.

[0068] In preferred embodiments, blood glucose reference readings areentered into the glucose monitor 100 periodically through out each dayof use. Preferably calibration is conducted immediately after theinitialization/stabilization of a glucose sensor 12 and once a daythereafter. However, calibration may be conducted more or less oftendepending on whether a glucose sensor 12 has been replaced, whether acalibration cancellation event has occurred, the stability of theglucose sensor 12 sensitivity over time, or the like.

[0069] In preferred embodiments, blood glucose reference readings arecollected several times per day but a new calibration factor iscalculated only once per day. Therefore, typically more than one pairedcalibration data point is collected between calibrations. In alternativeembodiments, the glucose monitor is calibrated every time a new pairedcalibration data point is collected.

[0070] Preferred embodiments use a single-point calibration technique(shown in a block diagram of FIG. 13) to calculate the SR when only onepaired calibration data point is available, such as immediately afterinitialization/stabilization. And a modified linear regression technique(shown in a block diagram in FIG. 15) is used when two or more pairedcalibration data points are available. Particular embodiments use asingle-point calibration technique whether or not more than one pairedcalibration data point is available.

[0071] A single-point calibration equation is based on the assumptionthat the Valid ISIG will be 0 when the blood glucose is 0. As shown inFIG. 12, a single paired calibration point 700 is used with the point(0,0) to establish a line 702. The slope of the line from the origin(0,0) and passing through the single paired calibration point 700 is thesingle-point sensitivity ratio (SPSR). The single-point calibrationequation to calculate the calibration factor SPSR is as follows:${SPSR} = \frac{{Blood}\quad {Glucose}\quad {Reference}\quad {Reading}}{{Valid}\quad {ISIG}}$

[0072] Where SPSR=a Single-Point Sensitivity Ratio.

[0073] Therefore, the calibrated blood glucose level is,

Blood Glucose Level=Valid ISIG*SPSR.

[0074] As an example, using the values of 20.1 Nano-Amps and 102 mg/dlfrom the paired calibration data point shown in FIG. 12, the calculationof SPSR is:

SPSR=102/20.1=5.07 mg/dl per Nano-amp.

[0075] To continue the example, once the calibration is complete, givena glucose sensor reading of 15.0 Nano-Amps, the calculated blood glucoselevel is:

Blood Glucose Level=15.0*5.07=76.1 mg/dl.

[0076] Additionally, particular embodiments use an offset value in acalibration equation to compensate for the observation that moresensitive glucose sensors 12 (i.e. glucose sensors 12 that generatehigher ISIG values compared to other glucose sensors 12 at the sameblood glucose level, which result in lower SR values) often have a lesslinear performance at very high blood glucose levels when compared toglucose sensors 12 with lower sensitivity (and therefore relativelyhigher SR values). If the SPSR for a particular glucose sensor 12, ascalculated above, is less than a sensitivity threshold value, then amodified SPSR (MSPSR) is calculated using an offset value included in amodified single-point calibration equation. In preferred embodiments,the threshold value is 7. When the initial calculation of the SPSR(shown above) is less than 7, an offset value of 3 is used to calculatethe MSPSR. If the initial calculation of SPSR yields a value of 7 orgreater, then the offset value is 0. Thus, the calibration factor(MSPSR) is calculated using the offset value in the modifiedsingle-point calibration equation, as follows:${MSPSR} = \frac{{Blood}\quad {Glucose}\quad {Reference}\quad {Reading}}{\left( {{Valid}\quad {ISIG}\text{-}{offset}} \right)}$

[0077] Therefore, the calibrated blood glucose level is,

Blood Glucose Level=(Valid ISIG-offset)*MSPSR.

[0078] Continuing the above example since the SPSR is 5.07, which isless than 7, the sensitivity ratio is recalculated using the MSPSRequation as:

MSPSR=102/(20.1−3)=5.96 mg/dl per Nano-amp.

[0079] And given a glucose sensor reading of 15.0 Nano-Amps aftercalibration the calculated blood glucose level is:

Blood Glucose Level=(15.0−3)*5.96=71.5 mg/dl.

[0080] In another example, given a blood glucose reference reading of 95from a typical blood glucose meter and a Valid ISIG value of 22.1, theresulting SPSR is 95/22.1=4.3. Since SR <7, the offset=3. Therefore, theMSPSR is 95/[22.1−3]=5.0. Note that when the SPSR is greater than orequal to 7 the offset value is 0 and therefore the MSPSR=SPSR.

[0081] In alternative embodiments, the offset value is eliminated fromthe equation for calculating the blood glucose value as follows:

Blood Glucose Level=Valid ISIG*MSPSR.

[0082] The threshold value of 7 and the associated offset of 3 have beenempirically selected based on the characteristics observed from testinga particular type of glucose sensors 12, such as those described in U.S.Pat. No. 5,391,250 entitled “Method of Fabricating Thin Film Sensors”,and U.S. patent application Ser. No. 60/121,655 filed on Feb. 25, 1999,entitled “Glucose Sensor Set”, incorporated by reference herein. Otherthreshold values may be used in conjunction with other offset values tooptimize the accuracy of the calculated MSPSR for various types ofglucose sensors 12 and sensors used to detect other bodycharacteristics. In fact, many threshold values may be used to selectbetween many offset values. An example using two different thresholdvalues (4 and 7) to select between three different offset values (5, 3and 0) follows:

If the SPSR <4, then use an offset value of 5, else if 4<=SPSR <7, thenuse an offset value of 3, else if SPSR >=7 use an offset value of 0.

[0083] In preferred embodiments the MSPSR is compared to a validsensitivity range to determine if the newly calculated MSPSR isreasonable. In order to identify potential system problems, a validMSPSR range of 1.5 to 15 is employed. This range has been determinedbased upon valid glucose sensor sensitivity measurements made in-vitro.MSPSR values outside this range result in a calibration error alarm (CALERROR) to notify the user of a potential problem. Other validsensitivity ranges may be applied depending on the types of sensors tobe calibrated, the range of acceptable sensitivity levels for thevarious sensor types, the manufacturing consistency expected for thesensors, environmental conditions, how long the sensor has been in use,or the like.

[0084] Preferred embodiments augment the single-point calibrationtechnique using a modified linear regression technique (shown in a blockdiagram in FIG. 15) when more than one paired calibration data point isavailable. As shown in FIG. 14, the paired calibration data points 800are linearly regressed by a least squares method to calculate the bestfit straight line 802 correlated with paired calibration data points800. The slope of the line resulting from the linear regression is thelinear regression sensitivity ratio (LRSR) used as the calibrationfactor to calibrate the glucose monitor 100. The linear regressioncalibration equation is as follows:${LRSR} = \frac{\sum\limits_{i = 1}^{N}\quad \left\lbrack {X_{i}Y_{i}} \right\rbrack}{\sum\limits_{i = 1}^{N}\quad \left\lbrack X_{i}^{2} \right\rbrack}$

[0085] Where X_(i) is the ith Valid ISIG value of paired calibrationdata points,  Y_(i) is the ith Blood Glucose Reference Reading of pairedcalibration data points and,  N is the total number of pairedcalibration data points used for calibration.  i is the identificationnumber of a particular paired calibration data point.

[0086] Therefore, the calibrated blood glucose level is,

Blood Glucose Level=Valid ISIG*LRSR.

[0087] Note that this linear regression uses a fixed intercept of zero(in other words, when the Valid ISIG is 0 the blood glucose value is 0)and therefore the linear regression method estimates only one regressionparameter, the slope. In alternative embodiments, other linearregression methods may be used that estimate additional regressionparameters such as an offset value.

[0088] Additionally, particular embodiments use an offset value in amodified linear regression calibration equation. The purpose of theoffset value, as described above for the single-point calibration, is tocompensate for the observation that more sensitive glucose sensors 12often have a less linear performance at very high blood glucose levels.If the LRSR for a particular glucose sensor 12, as calculated in thelinear regression calibration equation above, is less than a sensitivitythreshold value, then a modified linear regression sensitivity ratio(MLRSR) is calculated using an offset value included in a modifiedlinear regression calibration equation. In preferred embodiments, thethreshold value is 7. When the initial calculation of the LRSR is lessthan 7, an offset value of 3 is used to calculate the MLRSR. If theinitial calculation of LRSR yields a value of 7 or greater, then theoffset value is 0. Thus, the MLRSR is calculated using the offset valuein the modified linear regression calibration equation, thus:${MLRSR} = \frac{\sum\limits_{i = 1}^{N}\quad \left\lbrack {\left( {X_{i} - {offset}} \right)Y_{i}} \right\rbrack}{\sum\limits_{i = 1}^{N}\quad \left( {X_{i} - {offset}} \right)^{2}}$

[0089] Therefore, the calibrated blood glucose level is,

Blood Glucose Level=(Valid ISIG−offset)*MLRSR.

[0090] Just as in the case of single-point calibration techniquesdescribed above, other threshold values may be used in conjunction withother offset values in the modified linear regression calibrationequation to optimize the accuracy of the calculated MLRSR for varioustypes of glucose sensors 12 and other characteristic sensors.

[0091] In preferred embodiments the MLRSR is compared to a validsensitivity range to determine if the newly calculated MLRSR isreasonable. In order to identify potential system problems, a validMLRSR range of 2.0 to 10.0 is employed. MLRSR values outside this rangeresult in a calibration error alarm (CAL ERROR) to notify the user of apotential problem. As described above for the single-point calibrationtechniques, other valid sensitivity ranges may be applied.

[0092] In preferred embodiments, the glucose monitor data is linearlyregressed over a 24 hour period (or window), and new sensitivity ratiosare used for each 24 hour time period. In alternative embodiments, thetime period may be reduced to only a few hours or enlarged to cover theentire monitoring period with the glucose sensor (i.e., several days—oreven weeks with implanted sensors). In further embodiments, the timewindow may be fixed at a predetermined size, such as 24 hours, 12 hours,6 hours, or the like, and the window is moved along over the operationallife of the sensor.

[0093] In particular embodiments, paired calibration data points frommeasurements taken before the last calibration may be used to calculatea new sensitivity ratio. For example, to calibrate the glucose monitorevery 6 hours, a paired calibration data point is established every 6hours. And the linear regression technique described above is executedusing 4 paired calibration data points, the most recently acquired pointand points from 6, 12 and 18 hours before. Alternatively, the number ofpaired calibration data points used in the calibration may be as few asone or as large as the total number of paired calibration data pointscollected since the glucose sensor was installed. In alternativeembodiments, the number of paired calibration data points used in acalibration equation may grow or shrink during the life of the glucosesensor due to glucose sensor anomalies.

[0094] In still other embodiments, the decay characteristics of theglucose sensor 12 over time may be factored into the equation to accountfor typical degradation characteristics of the glucose sensor 12 due tosite characteristics, enzyme depletion, body movement, or the like.Considering these additional parameters in the calibration equation willmore accurately tailor the calibration equation used by the glucosemonitor 100 or post processor 200. In particular embodiments, otherparameters may be measured along with the blood glucose such as,temperature, pH, salinity, and the like. And these other parameters areused to calibrate the glucose sensor using non-linear techniques.

[0095] In a preferred embodiment, real-time calibration adjustment canbe performed to account for changes in the sensor sensitivity during thelifespan of the glucose sensor 12 and to detect when a sensor fails.FIG. 16 (in conjunction with FIGS. 17 and 18) describes the logic of aself-adjusting calibration technique to adjust the calibration formulaor detect a sensor failure in accordance with an embodiment of thepresent invention.

[0096] At block 1000, the user obtains a blood glucose reference from acommon glucose meter, or another blood glucose measuring device, andimmediately enters the blood glucose reference reading into the glucosemonitor 100. For every meter blood glucose entry, an instantaneouscalibration check is performed and compared to an expected range of thevalue of the calibration check, as in block 1010. In preferredembodiments, the Calibration Factor current is calculated (i.e.CFc=Meter BG/current ISIG value) to determine if the CFc (CalibrationFactor current) ratio is between 1.5 to 12 (“Criteria 1”), a minimumcriteria for an accurate ISIG value. If data is outside this range,raising the likelihood of a sensor failure or incorrectdetermination/entry of the meter BG value, a Cal Error alarm istriggered at block 1030 and the Recalibration Variable (Recal), which isoriginally set at NOFAIL is changed to FAILC1. At this point, anotherblood glucose reference reading is requested and entered into theglucose monitor 100 to determine whether there was indeed a sensorfailure or the Meter Blood Glucose value was incorrectly inputted. Theprevious MBGc that generated the error can be thrown out completely. IfCriteria 1 is again not satisfied at block 1010, an end of the sensorlife message will be generated at block 1040 since then the Recalvariable would be recognized as FAILC1 at block 1020. However, ifCriteria 1 is met at block 1010, then the logic proceeds to block 1200,where a check of the Recal Variable is made to see if Recal variable isnot equal to FAILC2. The Recal variable is set to FAILC2 only ifCriteria 2 a is not met, which will be discussed below. Given that theRecal variable at this point would only be set a NOFAIL or FAILC1, thelogic proceeds to block 1210.

[0097] At block 1210, a check is performed if the existing calibrationslope estimation (Previous Estimated Slope or PES) is much differentfrom the instantaneous calibration check (CFc) performed using the newmeter blood glucose value. A significant difference can indicate asensor failure. In the preferred embodiment, a difference between theprevious estimated slope (PES) and the current calibration check (CFc)in terms of percentage (threshold 1) and mg/dl (threshold 2) isperformed. Threshold 1 and 2 can be set depending on the particularsensor characteristics. An example of checking the changes between thePES and CFc is as follows:

Abs (1−PES/CFc)*100=Threshold 1 and

Abs (CFc−PES)*Isig=Threshold 2

[0098] If the percentage and/or absolute difference exceeds threshold 1and/or threshold 2 (collectively “Criteria 2 a”), then depending o theRecal variable (at block 1220), either trigger an end of sensor messageat block 1040 (if the Recal variable is equal to FAILC1 or FAILC2 atblock 1220) or a Cal Error alarm will be generated at block 1230 (if theRecal variable is equal to NOFAIL at block 1220). If a Cal Error alarmis generated at block 1230, the Recal variable is set to FAILC2, thecurrent meter blood glucose reading will be stored as MBGp (Meter BloodGlucose previous), and another blood glucose reference is requested andentered into the glucose monitor 100 (as MBGc) at block 1000. Byrequesting a new meter blood glucose reading, a comparison can be madebetween the last meter blood glucose reading stored at block 1230 andthe new meter blood glucose reading entered at block 1000 to determinewhether there was a sensor failure. The logic follows the same paths asdescribed above after block 1000 until the logic reaches block 1200. Atblock 1200, since Recal variable is now set to FAILC2 at block 1230, thedifference between the previous calibration check (CFp), which generatedthe FAILC2 alert, and the current calibration check (CFc) is performedat block 1300. In preferred embodiments, the difference between theprevious calibration check and the current calibration check in terms ofpercentage (threshold 1) and mg/dl (threshold 2) is performed. Inaddition, a check is performed on whether there has been a directionalchange between the CFp and CFc (collectively “criteria 2 b”). An exampleof criteria 2b is as follows:

Abs (1−CFp/CFc)*100=Threshold 1 and

Abs (CFc−CFp)*Isig=Threshold 2 and

(CFp−PES)*(CFc−CFp)>0

[0099] If the percentage and absolute difference exceeds threshold 1 andthreshold 2, and there is no directional change in the slope with thesecond blood glucose meter reading, then an end of sensor message willbe triggered at block 1040. If criteria 2 b is met, then the logicproceeds to block 1310. At block 1310, the logic then determines whetherthe difference between the previous value and the current value was dueto a change in sensitivity of the sensor or whether the reading ismerely noise. In the preferred embodiment, the determination of changein sensitivity versus noise is made by using Criteria 3 b. Criteria 3 bcompares the difference between (the previous estimated slope (PES) andthe current calibration check (CFc)) and (the previous calibration check(CFp) versus the current calibration check (CFc)) at block 1420. Forexample:

Abs (PES−CFc)<Abs (CFp−CFc)

[0100] As illustrated in FIG. 17a, if the difference between theestimated slope (PES) and the current calibration check (CFc) is lessthan the difference between the previous calibration check (CFp) and thecurrent calibration check (CFc), criteria 3 b will be met, indicatingthat the previous CFp is an outlier reading (i.e. an anomaly). Then, theMBGp (Meter Blood Glucose previous) is removed at block 1320 and onlythe MBGc paired with a valid ISIG is used in the slope calculation,which is resumed at block 1430 and applied in interpreting the sensorreadings at block 1130.

[0101] As illustrated in FIG. 17b, if criteria 3 b shows that differencebetween the estimated slope (PES) and the current calibration check(CFc) is greater than the difference between the previous calibrationcheck (CFp) and the current calibration check (CFc), criteria 3 b wouldnot be met, indicating a change in sensor sensitivity. The slopecalculation is then fine-tuned by creating a new (artificial) meterblood glucose value (MBGN) with a paired ISIG according to the lastslope (Seeding) at block 1330. Using the new paired MBG (MBGN) with thepaired MBGp and MBGc, the slope calculation is restarted (or reset) atblock 1340, as seen in FIG. 17b. The sensor calculation is thenperformed using the new slope calculation at block 1130. By resettingthe slope calculation, the slope calculation can thus be modifiedautomatically to account for changes in sensor sensitivity.

[0102] Continuing the logic from block 1210, if the percentage and/orabsolute difference between the PES and CFc is within threshold 1 and/orthreshold 2 at block 1210, indicating a valid calibration, the Recalvariable is again checked at block 1400. If the Recal variable is equalto FAILC1 (indicating that the meter BG was checked twice), anyfine-tuning determination is skipped and the MBGc paired with a validISIG is used to update the slope calculation at block 1430 and appliedin interpreting the sensor readings at block 1130. If the Recal Variableis not equal to FAILC1, then the logic will decide whether fine-tuningthe slope calculation is needed at blocks 1410 and 1420. In thepreferred embodiments, the decision to fine-tune is first made bycomparing the percentage and/or absolute difference between the PES andCFc (as done in block 1210) with a threshold 3 and/or a threshold 4(“Criteria 4”) at block 1410. For example:

Abs (1−PES/CFc)*100<Threshold 3 and

Abs (CFc−PES)*ISIG<Threshold 4

[0103] Again, threshold 3 and 4 can be determined based on theparticular sensor characteristics. If the percentage and/or absolutedifference between the PES and CFc is less than threshold 3 and/orthreshold 4 at block 1410 (i.e. Criteria 4 met), then the slopecalculation can simply be updated with the new MBGc and paired ISIGvalue at block 1430 and applied in interpreting the sensor readings atblock 1130.

[0104] On the other hand, if the Criteria 4 is not met at block 1410,the logic then determines at block 1420 whether the difference betweenthe expected value and the current value was due to a change insensitivity of the sensor or whether the reading is merely noise. In thepreferred embodiment, the determination of change in sensitivity versusnoise is made by using Criteria 3 a. Criteria 3 a compares thedifference between (the previous estimated slope (PES) and the previouscalibration check (CFp)) and (the current calibration check (CFc) versusthe previous calibration check (CFp)) at block 1420. For example:

Abs (PES−CFp)<Abs (CFc−CFp)

[0105] As seen in FIG. 18a, if the difference between the estimatedslope (PES) and the previous calibration check (CFp) is less than thedifference between the current calibration check (CFc) and the previouscalibration check (CFp), criteria 3 a will be met, indicating that theerror between the predicted value and the actual value for the CFc wasdue to noise in previous calibrations or beginning of a change in sensorsensitivity which will be picked up at the next calibration performance.The slope calculation is then simply updated with the new paired bloodglucose entry (MBGc) at block 1430 and applied in interpreting thesensor readings at block 1130.

[0106] As seen in FIG. 18b, if criteria 3 a shows that differencebetween the estimated slope (PES) and the previous valid calibrationcheck is greater than the difference between the previous validcalibration check (CFp) and the current calibration check (CFc),criteria 3 b would not be met, indicating a change in the sensorsensitivity and fine tuning is performed. Typically, fine tuning isperformed when two MBG entry in succession indicate a change in slope.The slope calculation is fine-tuned by creating a new (artificial) meterblood glucose value (MBGN) with a paired ISIG according to the lastslope (Seeding) at block 1330. Using the new paired MBG (MBGN) with thepaired MBGp and MBGc, the slope calculation is restarted (or reset) atblock 1340, as seen in FIG. 18b. The sensor calculation is thenperformed using the new slope calculation at block 1130. Again, byresetting the slope calculation, the slope calculation can thus bemodified automatically to account for changes in sensor sensitivity.

[0107] Alternative Calibration Techniques

[0108] Although the above description described the primary calibrationtechniques in the preferred embodiments, many modifications can be madeto the above described calibration techniques. For example, inalternative embodiments, the calibration factor may be calculated byfirst using a single-point technique to calculate the MSPSR for eachpaired calibration data point and then averaging them together, eitherunweighted or weighted by temporal order of by elapsed time. In otheralternative embodiments, other straight line curve fitting techniquesmay be used to calculate a slope to be used as the SR. In additionalalternative embodiments, other non- regressive curve fitting techniquesmay be used to generate equations that express the blood glucose levelrelative to the Valid ISIG. The equations may be polynomial, parabolic,hyperbolic, asymptotic, logarithmic, exponential, Gaussian or the like.In these embodiments, the SR is not a single value (such as a slope) butrather an equation representing a curve that is used to convert theValid ISIG from the glucose sensor 12 to a blood glucose value in theglucose monitor 100 or a post processor 200. In addition, in using amore robust formula for approximating the slope, the different ISIG canbe given different weights, as to weigh the more recent ISIGs more thanthe older ISIGs. For example where there are contiguous 8 ISIGs (i.e.n=8) are available:${{Filtered}\quad {ISIG}_{(i)}} = \frac{\sum\limits_{i = {i - 7}}^{i}\quad {W_{i}*{RawISIGi}}}{\sum\limits_{i = {i - 7}}^{i}\quad W_{i}}$

[0109] where Weights (i)=W_(i)=[0.9231 0.7261 0.4868 0.2780 0.13530.0561 0.0198 0.0060] When contiguous 8 ISIGs are not available (n<8)(i.e. after initialization or after triple skips in transmission, theweighting formula would be as follows:${{{Filtered}\quad {ISIG}_{(i)}} = \frac{\sum\limits_{i = {i - {({n - 1})}}}^{i}\quad {W_{i}*{RawISIGi}}}{\sum\limits_{i = {i - {({n - 1})}}}^{i}\quad W_{i}}},$

[0110] where n=number of contiguous ISIGs. Once all paired meterBGs/ISIGs (Pairing weights) have been weight distributed, the modifiedregression equation shall generate the slope. In a preferred alternativeembodiment, a Gaussian function$\frac{1}{\sqrt{2\pi}}e^{\frac{- x^{2}}{2\sigma^{2}}}$

[0111] is used to curve fit the sensor data, including the weightingfunctions, the Gaussian Slope is calculated using a modified regressionmodel such as:${Gaussian\_ slope} = \frac{\sum\quad {{PW}_{i} \times \left( {Filtered\_ Isig}_{i} \right) \times {MBG}_{i}}}{\sum\quad {{PW}_{i} \times \left( {Filtered\_ Isig}_{i} \right)^{2}}}$

[0112] where i=number of pairs in Gaussian buffer and${PW}_{i} = e^{\frac{- {({{Ti} - {Tc}})}^{2}}{2\sigma^{2}}}$

[0113] where Tc is the current time, Ti is Paired MBG/Filtered ISIGTimes and σ=15 hours (or 180 records, which is the width of the Gaussianprofile).

[0114] As discussed, preferred embodiments utilize a least squareslinear regression equation to calibrate the glucose monitor 100 orpost-processor 200 to analyze the sensor data. However, alternativeembodiments may utilize a multiple component linear regression, orequations with more variables than just the paired calibration datapoints, to account for additional calibration effecting parameters, suchas environment, the individual user's characteristics, sensor lifetime,manufacturing characteristics (such as lot characteristics),deoxidization, enzyme concentration fluctuation or degradation, powersupply variations, or the like. Still other alternative embodiments mayutilize singular and multiple, non-linear regression techniques.

[0115] In preferred embodiments, after the first calibration isperformed on a particular glucose sensor 12, subsequent calibrationsemploy a weighted average using a sensitivity ratio (SPSR, MSPSR, LRSR,or MLRSR) calculated from data collected since the last calibration, andprevious sensitivity ratios calculated for previous calibrations. So theinitial sensitivity ratio (SR1) is calculated immediately afterinitialization/stabilization using a paired calibration data point andis used by the glucose monitor 100 or the post processor 200 until thesecond sensitivity ratio (SR2) is calculated. The second sensitivityratio (SR2) is an average of SRI and the sensitivity ratio as calculatedusing the paired calibration data points since the initial calibration(SRday1). The equation is as follows:${SR2} = \frac{\left( {{SR1} + {SRday1}} \right)}{2}$

[0116] The third sensitivity ratio (SR3) is an average of SR2 and thesensitivity ratio as calculated using the paired calibration data pointssince the second calibration (SRday2). The equation is as follows:${SR3} = \frac{\left( {{SR2} + {SRday2}} \right)}{2}$

[0117] The sensitivity ratios for successive days use the same format,which is expressed below in generic terms:${SR}_{n} = {\frac{\left( {{SR}_{({n - 1})} + {SRday}_{({n - 1})}} \right)}{2}.}$

[0118] Where SR_(n) is the new sensitivity ratio calculated at thebeginning of time period, n, using data from time period (n−1), to beused by a real time glucose monitor 100, to convert Valid ISIGs to bloodglucose readings throughout time period, n.

[0119] SR_((n−1)) is the previous sensitivity ratio calculated at thebeginning of time period, n−1, using data from time period (n−2).

[0120] SRday_((n−1)) is the sensitivity ratio calculated using pairedcalibration data points collected since the last calibration.

[0121] Alternatively, the previous sensitivity ratios may be ignored andthe SR is calculated using only the paired calibration data points sincethe last calibration. Another alternative is to equally average allprevious SRs with the latest SR calculated using only the pairedcalibration data points since the last calibration. In alternativeembodiments, the paired calibration data points are used to establish anequation for a curve representing SR over time. The curve is then usedto extrapolate SR to be used until the next paired calibration datapoint is entered.

[0122] In embodiments that use a post processor 200 to evaluate thesensitivity ratio, the sensitivity ratio is calculated using pairedcalibration data points over a period of time since the last calibrationand is not averaged with previous SRs. The sensitivity ratio for aperiod of time can then be applied to the same period of time over whichthe paired calibration data points were collected. This is more accuratethan the real-time case described above for the glucose monitor 100because, in the real-time case, sensitivity ratios from a previous timeperiod must be used to calculate the blood glucose level in the presenttime period. If the sensitivity ratio has changed over time, thecalculation of blood glucose using an old sensitivity ratio introducesan error.

[0123] In particular embodiments, once calibration is complete, ValidISIG values are converted to blood glucose readings based on aparticular version of the sensitivity ratio, and the resulting bloodglucose readings are compared to an out-of-range limit. If the resultingcalculated blood glucose level is greater than a maximum out-of-rangelimit of 200 mg/dl (or equivalently 3600 mmol/l), the out-of-range alarmis activated. This is a calibration cancellation event, therefore, ISIGvalues are no longer valid once this alarm is activated. The bloodglucose readings are either not calculated, or at least not consideredreliable, until the glucose monitor 100 or post processor 200 isre-calibrated. The user is notified of the alarm and that re-calibrationis needed.

[0124] In alternative embodiments, higher or lower maximum out-of-rangelimits may be used depending on the sensor characteristics, thecharacteristic being measured, the user's body characteristics, and thelike. In particular embodiments, a minimum out-of-range limit may beused or both a maximum and a minimum out-of-range limits may be used. Inother particular embodiments, the out-of-range limits do not cause theblood glucose readings to become invalid and/or re-calibration is notrequired; however, an alarm could still be provided. In additionalparticular embodiments, more than one ISIG value must exceed anout-of-range limit before an alarm is activated of a calibrationcancellation event is triggered. The ISIG values that are out-of-rangeare not used to display a blood glucose value.

[0125] In alternative embodiments, calibration is conducted by injectinga fluid containing a known value of glucose into the site around theglucose sensor set 10, and then one or more glucose sensor readings aresent to the glucose monitor 100. The readings are processed (filtered,smoothed, clipped, averaged, and the like) and used along with the knownglucose value to calculate the SR for the glucose sensor 12. Particularalternative embodiments, use a glucose sensor set of the type describedin U.S. Pat. No. 5,951,521 entitled “A Subcutaneous Implantable SensorSet Having the Capability To Remove Or Deliver Fluids To An InsertionSite”.

[0126] In other alternative embodiments, the glucose sensor 12 issupplied with a vessel containing a solution with a known glucoseconcentration to be used as a reference, and the glucose sensor 12 isimmersed into the reference glucose solution during calibration. Theglucose sensor 12 may be shipped in the reference glucose solution. Asdescribed above, the glucose sensor readings are used to calculate asensitivity ratio given the known glucose concentration of the solution.

[0127] In another alternative embodiment, the glucose sensors 12 arecalibrated during the manufacturing process. Sensors from the samemanufacturing lot, that have similar properties, are calibrated using asampling of glucose sensors 12 from the population and a solution with aknown glucose concentration. The sensitivity ratio is provided with theglucose sensor 12 and is entered into the glucose monitor 100 or thepost processor 200 by the user or another individual.

[0128] In addition, although he preferred logic of FIG. 18 describedspecific operations occurring in a particular order, in alternativeembodiments, certain of the logic operations may be performed in adifferent order, modified, or removed and still implement the preferredembodiments of the present invention. Moreover, steps may be added tothe above described logic and still conform to the preferredembodiments. For example, although in the preferred embodiment of FIG.16, the Recal variable is never reset to no fail, potentially, anadditional step of can be added to reset the Recal variable to no failif no cal error alarms are triggered after a predetermined number ofcalibrations.

[0129] Therefore, while the description above refers to particularembodiments of the present invention, it will be understood that manymodifications may be made without departing from the spirit thereof. Theaccompanying claims are intended to cover such modifications as wouldfall within the true scope and spirit of the present invention.

[0130] The presently disclosed embodiments are therefore to beconsidered in all respects as illustrative and not restrictive, thescope of the invention being indicated by the appended claims, ratherthan the foregoing description, and all changes which come within themeaning and range of equivalency of the claims are therefore intended tobe embraced therein.

What is claimed is:
 1. A method of calibrating sensor data collectedfrom a sensor, wherein a calibration formula based on past calibrationsis used to interpret the sensor data, the method comprising the stepsof: obtaining a calibration reference value for the sensor; calculatinga current calibration factor based on the calibration reference valueand a current sensor data point; identifying either a possible error orchange in sensitivity in the sensor from the current calibration factor;and confirming a sensor failure or recognizing a change in sensorsensitivity.
 2. The method of claim 1, further comprising: updating thecalibration formula with the calibration reference value if the sensorfailure is not confirmed and no change in sensor sensitivity isrecognized; and interpreting the collected sensor data using the updatedcalibration formula.
 3. The method of claim 1, wherein the step ofconfirming a sensor failure or recognizing a change in sensorsensitivity further comprises: comparing the current calibration factorwith an estimated value determined from the calibration formula and apast calibration factor; and confirming a sensor failure when at leasttwo unexpected calibration factors are received in succession withoutsupporting each other.
 4. The method of claim 3, wherein the step ofconfirming a sensor failure or recognizing a change in sensorsensitivity further comprises: recognizing a change in sensorsensitivity when an unexpected past calibration factor is supported by asubsequent current calibration factor in succession; restarting thecalibration formula calculation when a change in sensor sensitivity isrecognized.
 5. The method of claim 4, further comprising: interpretingthe collected sensor data using the restarted calibration formula. 6.The method of claim 4, wherein the step of restarting the calibrationformula calculation further comprises: creating an artificialcalibration reference value; and formulating the calibration formulausing the artificial calibration reference value, a past calibrationreference value and a current calibration reference value.
 7. The methodof claim 2, wherein the calibration formula is a modified regressionmethod.
 8. The method of claim 7, wherein the modified regression methodis Gaussian regression method.
 9. The method of claim 7, wherein thecalibration formula weights past calibrations based on how recent thecalibration was performed.
 10. The method of claim 2, wherein thecalibration is performed while obtaining the sensor data.
 11. The methodof claim 1, wherein the sensor is a glucose sensor.
 12. The method ofclaim 1, wherein the current sensor data point is obtained by the stepsof: sampling characteristic sensor data at a predetermined rate from asensor over time; deriving at least one current sensor data point fromthe sampled characteristic sensor data at a predetermined memory storagerate.
 13. The method of claim 1, wherein the current sensor data pointis obtained by the steps of: sampling characteristic sensor data;deriving interval values by applying clipping limits and averaging thepost-clipped sampled characteristic sensor data over a predeterminedinterval rate; and deriving at least one current sensor data point byaveraging the derived interval values at a predetermined memory storagerate.
 14. An apparatus for calibrating sensor data collected from asensor, wherein a calibration formula based on past calibrations is usedto interpret the sensor data, the apparatus comprising: means forobtaining a calibration reference value for the sensor; means forcalculating a current calibration factor based on the calibrationreference value and a current sensor data point; means for identifyingeither a possible error or change in sensitivity in the sensor from thecurrent calibration factor; and means for confirming a sensor failure orrecognizing a change in sensor sensitivity.
 15. The apparatus of claim14, further comprising: means for updating the calibration formula withthe calibration reference value if the sensor failure is not confirmedand no change in sensor sensitivity is recognized; and means forinterpreting the collected sensor data using the updated calibrationformula.
 16. The apparatus of claim 14, wherein the means for confirminga sensor failure or recognizing a change in sensor sensitivity furthercomprises: means for comparing the current calibration factor with anestimated value determined from the calibration formula and a pastcalibration factor; and means for confirming a sensor failure when atleast two unexpected calibration factors are received in successionwithout supporting each other.
 17. The apparatus of claim 16, whereinthe means for confirming a sensor failure or recognizing a change insensor sensitivity further comprises: means for recognizing a change insensor sensitivity when an unexpected past calibration factor issupported by a subsequent current calibration factor in succession;means for restarting the calibration formula calculation when a changein sensor sensitivity is recognized.
 18. The apparatus of claim 17,further comprising: means for interpreting the collected sensor datausing the restarted calibration formula.
 19. The apparatus of claim 17,wherein the means for restarting the calibration formula calculationfurther comprises: means for creating an artificial calibrationreference value; and means for formulating the calibration formula usingthe artificial calibration reference value, a past calibration referencevalue and a current calibration reference value.
 20. The apparatus ofclaim 15, wherein the calibration formula is a modified regressionmethod.
 21. The apparatus of claim 20, wherein the modified regressionmethod is Gaussian regression method.
 22. The apparatus of claim 20,wherein the calibration formula weights past calibrations based on howrecent the calibration was performed.
 23. The apparatus of claim 15,wherein the calibration is performed while obtaining the sensor data.24. The apparatus of claim 14, wherein the sensor is a glucose sensor.25. The apparatus of claim 14, wherein the current sensor data point isobtained by: means for sampling characteristic sensor data at apredetermined rate from a sensor over time; means for deriving at leastone current sensor data point from the sampled characteristic sensordata at a predetermined memory storage rate.
 26. The method of claim 14,wherein the current sensor data point is obtained by: means for samplingcharacteristic sensor data; means for deriving interval values byapplying clipping limits and averaging the post-clipped sampledcharacteristic sensor data over a predetermined interval rate; and meansfor deriving at least one current sensor data point by averaging thederived interval values at a predetermined memory storage rate.
 27. Anarticle of manufacture containing code for calibrating sensor datacollected from a sensor, wherein a calibration formula based on pastcalibrations is used to interpret the sensor data, comprising a computerusable media including at least one embedded computer program that iscapable of causing at least one computer to perform: obtaining acalibration reference value for the sensor; calculating a currentcalibration factor based on the calibration reference value and acurrent sensor data point; identifying either a possible error or changein sensitivity in the sensor from the current calibration factor; andconfirming a sensor failure or recognizing a change in sensorsensitivity.
 28. The article of manufacture of claim 27, furtherperforming: updating the calibration formula with the calibrationreference value if the sensor failure is not confirmed and no change insensor sensitivity is recognized; and interpreting the collected sensordata using the updated calibration formula.
 29. The article ofmanufacture of claim 27, wherein the step of confirming a sensor failureor recognizing a change in sensor sensitivity further performs:comparing the current calibration factor with an estimated valuedetermined from the calibration formula and a past calibration factor;and confirming a sensor failure when at least two unexpected calibrationfactors are received in succession without supporting each other. 30.The article of manufacture of claim 29, wherein the step of confirming asensor failure or recognizing a change in sensor sensitivity furtherperforms: recognizing a change in sensor sensitivity when an unexpectedpast calibration factor is supported by a subsequent current calibrationfactor in succession; restarting the calibration formula calculationwhen a change in sensor sensitivity is recognized.
 31. The article ofmanufacture of claim 30, further performing: interpreting the collectedsensor data using the restarted calibration formula.
 32. The article ofmanufacture of claim 30, wherein the step of restarting the calibrationformula calculation further performs: creating an artificial calibrationreference value; and formulating the calibration formula using theartificial calibration reference value, a past calibration referencevalue and a current calibration reference value.
 33. The article ofmanufacture of claim 28, wherein the calibration formula is a modifiedregression method.
 34. The article of manufacture of claim 33, whereinthe modified regression method is Gaussian regression method.
 35. Thearticle of manufacture of claim 33, wherein the calibration formulaweights past calibrations based on how recent the calibration wasperformed.
 36. The article of manufacture of claim 28, wherein thecalibration is performed while obtaining the sensor data.
 37. Thearticle of manufacture of claim 27, wherein the sensor is a glucosesensor.
 38. The article of manufacture of claim 27, wherein the currentsensor data point is obtained by performing the steps of: samplingcharacteristic sensor data at a predetermined rate from a sensor overtime; deriving at least one current sensor data point from the sampledcharacteristic sensor data at a predetermined memory storage rate. 39.The method of claim 27, wherein the current sensor data point isobtained by performing the steps of: sampling characteristic sensordata; deriving interval values by applying clipping limits and averagingthe post-clipped sampled characteristic sensor data over a predeterminedinterval rate; and deriving at least one current sensor data point byaveraging the derived interval values at a predetermined memory storagerate.